Volume 27 Issue 4 (October-December 2011)

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Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting

Dass, M., Jank, W., Shmueli, G.
Pages 1259-1270

This paper presents a novel intelligent bidding system, called SOABER (Simultaneous Online Auction BiddER), which monitors simultaneous online auctions of high-value fine art items. It supports decision-making by maximizing bidders' surpluses and their chances of winning an auction. One key element of the system is a dynamic forecasting model, which incorporates information about the speed of an auction's price movement, as well as the level of competition both within and across auctions. Other elements include a wallet estimator, which gauges the bidders' willingness to pay, and a bid strategizer, which embeds the forecasting model into a fully automated decision system. We illustrate the performance of our intelligent bidding system on an authentic dataset of online art auctions for Indian contemporary art. We compare our system with several simpler ad-hoc approaches, and find it to be more effective in terms of both the extracted surplus and the resulting winning percentage.

Keywords: Bidder surplus optimization, Dynamic price forecasting, Functional data analysis, Intelligent bidding system, Simultaneous online art auctions
sample of the R code used in the paper
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For more information on the
Mayukh Dass, 08 May 2012

For more information on the data, please consult other papers using the same/ similar dataset and listed at http://www.mdass.com/pub_auc.html